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PhyBench benchmark
AI model leaderboard for the PhyBench benchmark. Compare how large language models score on PhyBench, see the full ranking, and understand what this AI benchmark measures. gemini-2.5-pro currently leads with 55.01. Physics-problem reasoning benchmark — high-school and undergraduate physics questions with numeric answers.
Leaderboard
| # | Model | Organization | Score | Variant | Source |
|---|---|---|---|---|---|
| #1 | gemini-2.5-pro | Google DeepMind | 55.01 | cited-ring-1t | official ↗ |
| #2 | GPT-5 | OpenAI | 48.53 | thinking-high-cited-ring-1t | official ↗ |
| #3 | DeepSeek-V3.1-Terminus | DeepSeek | 47.91 | thinking-cited-ring-1t | official ↗ |
| #4 | Ring-1T | Ant Group | 42.65 | thinking | official ↗ |
| #5 | Qwen3-235B-A22B-Thinking (Jul 2025) | Qwen | 42.61 | thinking-cited-ring-1t | official ↗ |
Frequently asked questions about PhyBench
What is the PhyBench benchmark?
Physics-problem reasoning benchmark — high-school and undergraduate physics questions with numeric answers.
How is the PhyBench benchmark scored?
PhyBench is scored using the accuracy metric, where a higher score is better. GenAIList aggregates reported scores from model providers and papers into a single ranked leaderboard.
Which AI model scores highest on PhyBench?
As of the latest reported scores on GenAIList, gemini-2.5-pro achieves the highest result on PhyBench with a score of 55.01.
Is a higher PhyBench score better?
Yes. On PhyBench a higher score indicates better performance, so models near the top of the leaderboard are the strongest.